Sustainability of product returns
Bibliographic record
Abstract
Product returns are harmful to the environment due to increased transportation, waste and emissions (Frei et al., 2020). Whilst literature recognises the importance of sustainability in operations and supply chains, there is limited research on the environmental impact of returns and retailers’ views on sustainability in returns. To address these gaps, the first phase of this research consisted of a review of academic and other publications (e.g., blogs and reports by reverse logistics companies) to gain a comprehensive picture of the current product returns sustainability research and practice. The second phase was to undertake semi-structured interviews with retailers in the UK and Canada. We also participated in member sessions with the Efficient Consumer Response (ECR) Retail Loss Group to learn about the risks of implementing a sustainability strategy in returns under uncertain conditions in a pandemic world. The results from our first phase indicate that most sustainability studies to date excluded the analysis of returns (e.g., Mangiaracina et al., 2015). Although environmental assessment methods, such as Material flow analysis and Life-cycle assessment (Withanage and Habib, 2021), can assist practitioners in making more sustainable decisions in their forward supply chains, no study has proposed a systematic evaluation of the environmental impacts of reverse supply chains. Future research should assess this to help reduce the ecological damage caused by returns. The interviews conducted in the second phase revealed that retailers have only recently started paying attention to the returns’ financial impact. Many commented that they have not gained enough experience yet to manage returns sustainably. This is exacerbated by the lack of data and communications on returns’ costs and wastes ecologically. Additionally, the reverse logistics on returned products need further sustainable development. The scientific understanding of returns and their degrees of (un-)sustainability is still at an early stage. Retailers also highlighted that because of the pandemic, increased returns resulted in excessive transportation and processing (e.g., returned products needing quarantine). They are uncertain whether current customers’ returns behaviours will persist after the pandemic and are concerned about more unnecessary waste. Based on the obtained results, we produced recommendations on improving the ecological sustainability of returns. Overall, our study has made both practical and theoretical contributions in the field of sustainable returns operations and circular economy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".